Traditional and Machine-Learning-Based Approaches for Annotation of Natural Products

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 228

Special Issue Editors


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Guest Editor
Brightseed, South San Francisco, CA 94080, USA
Interests: metabolomics; compound identification; cheminformatics

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Guest Editor
Genome Center, University of California, Davis, CA 95616, USA
Interests: metabolite identification; metabolomics; proteomics

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Guest Editor
Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, IL 60607, USA
Interests: metabolite identification; metabolomics; drug discovery

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue of the journal Metabolites, dedicated to “Traditional and Machine-Learning-Based Approaches for Annotation of Natural Products". This Issue aims to bring together novel research and advancements in the interdisciplinary field of metabolomics and cheminformatics, with a particular emphasis on mass-spectrometry-based bioactive compound identification.

We welcome manuscripts that present innovative methodologies, algorithms, and software tools that leverage cheminformatics to annotate and identify natural products and their metabolites as well as any manual/heuristic approaches. We are interested in research that explores the use of mass spectrometry for comparing and interpreting complex spectra and for identifying novel bioactive compounds.

We also encourage submissions that delve into the discovery and identification of bioactive compounds, including the development and application of computational tools and databases. Contributions may encompass a broad range of topics, from the utilization of machine learning and artificial intelligence in compound identification to the integration of cheminformatics and metabolomics data for the discovery of naturally derived bioactive compounds.

This Special Issue aims to provide an international platform for researchers to share the latest developments and challenges in the rapidly evolving field of cheminformatics and metabolomics. We look forward to your contributions to this exciting area of research.

Dr. Ivana Blaženović
Dr. Yuanyue Li
Dr. Dejan C. Nikolić
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mass spectrometry
  • machine learning
  • bioactive compounds
  • metabolomics

Published Papers

This special issue is now open for submission.
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